Going the Wrong Way on a One-Way Street: Centrality in Physics and Biology*

Abstract When ideas and tools move from one field to another, the movement is generally from the natural to the social sciences. In recent years, however, there has been a major movement in the opposite direction. The idea of centrality and the tools for its measurement were originally developed in the social science field of social network analysis. But currently the concept and tools of centrality are being used widely in physics and biology. This paper examines how and why that-wrong way-movement developed, its extent and its consequences for the fields involved

Editing a Normal Science Journal in Social Science

Editer un journal de science normale dans les sciences sociales : Cet papier mets en évidence quelqu'unes des différences entre les journaux de "science normale" dans des disciplines telles que la physique et la chimie, et des "sciences non-normales" dans des disciplines de sciences sociales. L'article montre qu'un journal, Social Networks, ressemble plus à un journal de science normale qu'un journal typique des sciences sociales. L'auteur montre que les propriétés de sciences normales de la recherche sur des réseaux sociaux sont engendrées par l'utilisation des images graphiques et des modèles mathématiques, et la disponibilité d'ordinateurs capables d'analyser des ensembles de données à structures relativement complexes.This paper displays some differences between "normal science" journals in fields like physics and chemistry and those in "non-normal science" fields in the social sciences. It shows that one journal, Social Networks, looks more like a normal science journal than a typical social science journal. I argue that the normal science properties of social network research stem from its use of both graphic images and mathematical models and from the availability of computers that permit the analysis of relatively complex data structures

Centrality in valued graphs: A measure of betweenness based on network flow

A new measure of centrality, C,, is introduced. It is based on the concept of network flows. While conceptually similar to Freeman’s original measure, Ca, the new measure differs from the original in two important ways. First, C, is defined for both valued and non-valued graphs. This makes C, applicable to a wider variety of network datasets. Second, the computation of C, is not based on geodesic paths as is C, but on all the independent paths between all pairs of points in the network

The Irreducible Spine(s) of Undirected Networks

Using closure concepts, we show that within every undirected network, or graph, there is a unique irreducible subgraph which we call its "spine". The chordless cycles which comprise this irreducible core effectively characterize the connectivity structure of the network as a whole. In particular, it is shown that the center of the network, whether defined by distance or betweenness centrality, is effectively contained in this spine. By counting the number of cycles of length 3 <= k <= max_length, we can also create a kind of signature that can be used to identify the network. Performance is analyzed, and the concepts we develop are illurstrated by means of a relatively small running sample network of about 400 nodes.Comment: Submitted to WISE 201

Novel Edge and Density Metrics for Link Cohesion

We present a new metric of link cohesion for measuring the strength of edges in complex, highly connected graphs. Link cohesion accounts for local small hop connections and associated node degrees and can be used to support edge scoring and graph simplification. We also present a novel graph density measure to estimate the average cohesion across nodes. Link cohesion and the density measure are employed to demonstrate community detection through graph sparsification by maximizing graph density. Link cohesion is also shown to be loosely correlated with edge betweenness centrality